Importance of Microscale Climate Simulations in City Scale Overheating Assessments
Bibliographic record
Abstract
Abstract This study demonstrates the importance of high-resolution climate simulations when conducting city-scale outdoor heat wave alert and indoor overheating assessments. This is done by modelling urban climate of the Ottawa and Montreal cities at 1 km and 25 km, typical for regional climate modelling respectively, over the summer of 2018 when an extreme heat event caused around 100 deaths in these cities. It is shown that urban climate characteristics (higher temperatures, lower wind speeds, lower relative humidity in the urban core than surroundings) are better simulated at 1 km than at 25 km spatial resolution. Indoor conditions are simulated for an archetype model of a single detached house using EnergyPlus software for two locations within the cities: a) city center and b) airport location. It is shown that the simulated indoor air temperature in the building is highly correlated with the outdoor air temperature. Furthermore, it is found that the maximum indoor air temperature difference of the city center and the airport can be as high as 8°C in Montreal and 9°C in Ottawa. Such intra-urban differences in overheating in buildings will be ignored if microscale simulations are not performed, highlighting their importance for building overheating assessments in cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".